Methods of identification models of soil humidity

https://doi.org/10.29328/journal.acee.1001024 Review Article Methods of identifi cation models of soil humidity ZH Aliyev* Institute of Soil Science and Agrochemistry of the National Academy of Sciences of Azerbaijan, Azerbaijan More Information *Address for Correspondence: ZH Aliyev, Professor, Institute of Soil Science and Agrochemistry of the National Academy of Sciences of Azerbaijan, Azerbaijan, Tel: +994504242130; Email: zakirakademik@mail.ru Submitted: 09 March 2020 Approved: 02 June 2020 Published: 03 June 2020 How to cite this article: Aliyev ZH. Methods of identifi cation models of soil humidity. Ann Civil Environ Eng. 2020; 4: 034-037. DOI: 10.29328/journal.acee.1001024 Copyright: © 2020 Aliyev ZH. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

For the operational forecasting of the dynamics of moisture reserves, it is reduced to the prediction of precipitation and total evaporation (E). The remaining elements of the balance either do not change over time, are either known or are de ined as functions of P and E.
The plant's need for water E (evapotranspiration) is determined on the basis of the bioclimatic method in the modi ication of B.V. Danilchenko (2) by the formula: Where E is the total evaporation; To b is the biological coef icient of culture; KM -microclimatic coef icient.
In this case, evaporation is determined by the formula of Ivanov E = ki dt (v) (2) where Ki is the temperature coef icient characterizing the energy part of the evaporation; d -elasticity vapor; f (v) is the wind function.
When calculating the volatility per day, the temperature coef icient is determined by the formula: where t is the air temperature; Ia is the vapor pressure in mb.
De iciency of vapor pressure is de ined as where r is the relative humidity in percent.
Wind function can be determined by here vr is the veteran's speed 2 meters above the surface of the earth, in m / s; vf is the wind speed at the height of the wind vane.
One solution is to use rolling operational forecasts.
Operational irrigation plans are calculated once or twice a week, when forecasting moisture reserves in every 1st period 10 days before. In the next 1st period, actual changes in stocks over the past day are calculated.
Given the new initial wetting, a forecast is left for the next 10 days. For two equal variables P and E, only total evaporation is predicted promptly taking into account the expected variables, and the amount of precipitation is always taken equal to zero.
This approach is associated with the organization of irrigation: the prediction of rainfall mobilizes in preparation for irrigation, in the case of the prediction of heavy rainfall is easy to stop.
Operational forecasting of total evaporation E, Starting ield moisture is de ined as Where -h -active soil layer; b -average soil moisture, to the mass of dry soil; F j is the average soil density for the layer.
Moisture reserve at the end of the day Here Wτ -1 is the moisture supply at the end of the previous and the beginning of new days in mm; mτ -net irrigation rate (which is entered into the calculation if irrigation was carried out on the day) in mm; Wτq -capillary in lux of nearby groundwater, mm -for the decline phase, daily water consumption: The boundary between the phases of rise and fall was the end of the second decade of July.
Here W is the moisture reserve at the end of the decade, mm; X -moisture supply at the beginning of the decade and the amount of precipitation per decade; Z -average moisture de icit per decade mm; 0 Y -average decade temperature in C In [2], regression equations are presented that express the statistical relationship between the GTK parameter R SCC = ───── (13)

0, 1 ∑t
Where -∑t is the sum of air temperatures; P is the amount of precipitation in mm.
A signi icant factor, impacting on water consumption crops are their biological characteristics. It is believed that each biological species of a plant is characterized by its own de ined rhythm of development and the corresponding regime of water consumption.
The in luence of biological characteristics of crops on the dynamics of water consumption is taken into account using biological coef icients of total evaporation of Ki, determined by the formula: Where E is the water consumption for the entire growing season; ∑d is the sum of moisture de icits for that period, Kσ is the coef icient of the biological curve, determined by dividing the gross water low rate for the interphase period by the sum of water moisture de icits.
The biological curve coef icient is in luenced by many factors, in the area, weather conditions. Under different climatic conditions, their values can vary 1.5-2 times, and therefore, their re inement is required. In general, the soil moisture model has the form. https://doi.org/10.29328/journal.acee.1001024 dW q -------= φ (W q , а, T q , τ R , R, T, V TR, S, W 0 , Ĝ, T n , Q S, A) (15) dt Here W is the soil moisture.
It is advisable to replace such differential equations in identi ication problem such as above for ontogenesis with the difference ones obtained by integration. The quality of soil characteristics affecting its moisture content should be considered: Here, T is the type of soil  Identi ication was carried out using a step-by-step regression program using the inclusion method to select variables.
These indicators are signi icant when a large area with different soil characteristics in different areas is considered. However, for small areas, the task of adapting the soil moisture model is not necessary, and a one-time "Start" identi ication of its parameters is suf icient. It should, however, consider that when "small" amounts of land in the mountainous terrain, with heavy lateral in low adaptation humidity models https://doi.org/10.29328/journal.acee.1001024 Another model is also proposed to characterize the condition of natural moisture in the mountainous territories of Azerbaijan: (1-μ) X + W μ -W k a = ------------------------- E where, -μ is the drain coef icient, X is the amount of precipitation, W -moisture reserves in the calculated soil layer at the beginning and end of the estimated time period, E is evaporation.
Identi ication of the model soil moisture was carried out based on the data for the harvested area.